The present invention relates generally to magnetic resonance (MR) imaging and, more particularly, to a flexible approach for sampling and reconstructing an image of an imaging volume with multiple receiver coils to accelerate data acquisition.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or “longitudinal magnetization”, MZ, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated and this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
One technique that has been developed to accelerate MR data acquisition is commonly referred to as “parallel imaging” or “partial parallel imaging”. In parallel imaging, multiple receive coils acquire data from a region or volume of interest. Thus, parallel imaging is used to accelerate data acquisition in one or more dimensions by exploiting the spatial dependence of phased array coil sensitivity. Parallel imaging has been shown to be successful in reducing scan time, but also reducing image blurring and geometric distortions. Moreover, parallel imaging can be used to improve spatial or temporal resolution as well as increased volumetric coverage.
There are several types of parallel imaging reconstruction methods that have been developed to generate the final, unaliased image from accelerated data. These methods can generally be divided into two categories based on how they treat the reconstruction problem: 1) SENSE-based techniques (Sensitivity Encoding) estimate coil sensitivity profiles from low-resolution calibration images, which can then be used to unwrap aliased pixels in image space using a direct inversion algorithm; and 2) GRAPPA-based techniques (Generalized Auto-calibrating Partially Parallel Acquisition) calculate reconstruction weights necessary to synthesize unacquired data directly from acquired data in k-space using an algorithm that does not require coil sensitivity estimates. The reconstruction weights for GRAPPA are calculated from a small amount of fully sampled calibration data that is typically embedded within the scan (“auto-calibration”), but can also be acquired before or after the scan. While both SENSE- and GRAPPA-based approaches have been successful, in practice, GRAPPA-based techniques have been shown to be preferred when accurate coil sensitivity estimates cannot be obtained, for example, in reduced FOV applications.
One known GRAPPA technique operates entirely in k-space and uses only one-dimensional (1D) convolution kernels. A single set of 1D convolution kernel weights are determined in k-space and subsequently applied in k-space to reconstruct a full k-space data set for each coil. Each k-space data set is then Fourier transformed into a single image such that there is an image per coil. The coil images are combined, e.g., using sum-of-squares, to create a final image. This concept of reconstructing separate k-space data sets for each component coil is precisely what sets GRAPPA apart from its predecessor, VD-AUTO-SMASH. The combination of component coil magnitude images avoids any inter-coil phase errors and the weight generation on a per coil basis makes GRAPPA no longer require that the sensitivity profiles from the involved coils form spatial harmonics, such as needed for SMASH-based techniques.
In the GRAPPA method, the GRAPPA weights, a.k.a. 1D GRAPPA kernel, are estimated and applied only on neighboring data along the direction of acceleration (ky). This is not ideal for most coil configurations, since the sensitivity profiles vary not only in the phase-encoding direction (direction of acceleration) but also in the frequency-encoding direction, which is orthogonal to the acceleration direction. As such, it has been suggested that the accuracy of GRAPPA-based techniques can be improved by using a two-dimensional (2D) rather than a 1D k-space kernel. Moreover, it is believed that the 2D GRAPPA kernel improves the conditioning of the system matrix and therefore reduces reconstruction noise and residual errors. However, this accuracy comes at the expense of an increase in reconstruction time due to the computationally intensive 2D k-space convolution step.
It would therefore be desirable to have a parallel imaging technique that maintains the advantages of parallel imaging, (e.g., reduced scan time), is sufficiently flexible to account for various coil configurations such that variations in sensitivity profiles are considered, and provides significantly reduced reconstruction times.